Multi-layer Cooperative Optimization Operation Method for Rural Integrated Energy System

doi:10.12204/j.issn.1000-7229.2022.05.007

Abstract

Abstract:

Rural integrated energy system can effectively improve energy efficiency and economy while meeting the diversified energy demand of rural users through the coordination and complementarity of various energy sources. Firstly, considering adapting to typical rural scenarios, a three-layer cooperative self-regulation framework for hierarchical cooperative operation optimization of rural integrated energy is proposed. Then, according to the typical equipment of the rural integrated energy system, a combined optimization dispatching model of the three-layer rural integrated energy system, including source, storage and load, and the corresponding optimization dispatching process, are established. In the dispatching model, residual heat of biogas generator unit and air-source heat pump cooperate to provide heating for users in winter. In summer, the residual heat of biogas generator set is recovered, and the rural users are cooled by the combination of lithium bromide refrigerator and air-source heat pump. Finally, an example is given to analyze the multi-layer collaborative optimization method for rural integrated energy system. The results show that the optimization method can improve the economy of rural residents’ energy consumption, and the effectiveness of the proposed method is verified.

Key words: three-layers collaborative operation framework, rural integrated energy system, three-layer cooperative operation optimization scheduling model, residual heat of biogas unit

CLC Number:

TM926

LIU Xuefei, PANG Ning, WANG Yunjia, YAO Boyan, WEI Ziqiang, WEN Peng, GAO Liai. Multi-layer Cooperative Optimization Operation Method for Rural Integrated Energy System[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(5): 63-71.

Figures/Tables 19

Fig.1 Three-level hierarchical framework of collaborative optimization system

Fig.2 VIES rural integrated energy system

Fig.3 Interactions between models at various levels

Fig.4 Flow chart of three-level collaborative optimization scheduling

Fig.5 Typical daily power curve of WT

Fig.6 Typical daily power curve of photovoltaic power generation

Table 1 Time-of-use electricity price of Hebei power grid

Fig.7 System structure diagram of 3 VIES

Fig.8 Rural household distribution and heat network topology

Fig.9 Optimization results of typical winter days at TIES

Fig.10 Optimization results of a typical day in summer at TIES

Fig.11 Coordination and optimization results of a typical day of heating equipment in winter at VIES

Fig.12 Coordination and optimization results of a typical day of cooling equipment in summer at VIES

Fig.13 Costs before and after optimization of typical winter and summer days at VIES

Fig.14 The optimization results of VIES-1

Fig.15 The optimization results of VIES-2

Fig.16 The optimization results of VIES-3

Fig.17 Optimization results of UIES of a typical day in winter

Fig.18 Optimization results of UIES of a typical day in summer

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